Demystifying “drop-outs” in single-cell UMI data

Many existing pipelines for scRNA-seq data apply pre-processing steps such as normalization or imputation to account for excessive zeros or “drop-outs." Here, we extensively analyze diverse UMI data sets to show that clustering should be the foremost step of the workflow. We observe that most d...

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Detalles Bibliográficos
Autores principales: Kim, Tae Hyun, Zhou, Xiang, Chen, Mengjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412673/
https://www.ncbi.nlm.nih.gov/pubmed/32762710
http://dx.doi.org/10.1186/s13059-020-02096-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412673/
https://www.ncbi.nlm.nih.gov/pubmed/32762710
http://dx.doi.org/10.1186/s13059-020-02096-y

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